CN110445127A - A kind of var Optimization Method in Network Distribution and system towards multiple stochastic uncertainty - Google Patents
A kind of var Optimization Method in Network Distribution and system towards multiple stochastic uncertainty Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/16—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by adjustment of reactive power
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
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Abstract
A kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty includes the following steps: to obtain electric network data；Data are brought into the idle work optimization model constructed in advance；The idle work optimization model is solved by particle swarm algorithm, optimal solution is obtained and realizes GA for reactive power optimization；The idle work optimization model is with the minimum target of active loss, while equality constraint and inequality constraints.The randomness, the randomness of distributed generation resource power output and the randomness of reactive power compensator for considering distribution network load simultaneously, more accurately react power distribution network actual conditions.
Description
Technical field
The present invention relates to power distribution networks to run control technology field, and in particular to a kind of matching towards multiple stochastic uncertainty
Reactive power optimization method and system.
Background technique
GA for reactive power optimization problem has following characteristics: (1) non-linear.Objective function and constraint condition monarch's prestige are non-linear.
(2) discrete type.Adjustable transformer tap-c hange control number and compensating capacitor switching group number are all discrete variables.(3) complexity.
Equality constraint and inequality constraints are simultaneously deposited, and the number of constraint condition increases with the expansion of power grid scale.
Existing var Optimization Method in Network Distribution, can substantially be divided into four classes: classical algorithm, artificial intelligence approach,
Hybrid algorithm etc..With the development of current power distribution network, these algorithms have the following problems in practical applications: (1) distributed electrical
The grid-connected initiation network reconfiguration in source, existing idle work optimization model and algorithm not can accurately reflect the actual conditions of current system.
(2) with the extension of the complication of distribution net work structure and power distribution network scale, current optimization algorithm not can effectively solve various
The GA for reactive power optimization problem of scale.(3) current power distribution network, which controls real-time reactive power optimization, requires harshness, and main includes real-time
Response speed, starting point a robustness, infeasibility detection and handling, control variable smoothly effectively adjust, the quality of data require and
The factors such as external network equivalence, existing algorithm are difficult to realize the requirement of line closed loop control.(4) load model itself
Time variation and uncertainty cause the Dynamic reactive power optimization algorithm influenced by load variations to be difficult to meet its model needs, nothing
The result of function optimization often leads to isloation state variable and approaches restrained boundary, generates new out-of-limit.(5) existing hybrid algorithm is general
It is all that two kinds of algorithms are respectively independently solved, wherein a side only utilizes the calculated result of other side, not directly into searching for other side
Rope process, this hybrid mode are without any improvement to the performance of wherein each algorithm itself.
Summary of the invention
Present invention provide the technical scheme that
A kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty, includes the following steps:
Obtain electric network data；
Data are brought into the idle work optimization model constructed in advance；
The idle work optimization model is solved by particle swarm algorithm, optimal solution is obtained and realizes that power distribution network is idle excellent
Change；
The idle work optimization model is with the minimum target of active loss, while equality constraint and inequality constraints.
Preferably, the objective function is shown below:
Min f=E (Ploss)=∑ E (PLoss, i, j(PI, j, QI, j, PG, i, j, QG, i, j, CI, j))
In formula: E () is expectation；PlossFor distribution network total losses；PLoss, i, jFor the loss of branch i-j in distribution network；
PI, jFor the active power of branch i-j institute on-load；QI, jFor the reactive power of branch i-j institute on-load；PG, i, jFor branch i-j institute
The active power of distributed generation resource；QG, i, jFor the reactive power of branch i-j institute distributed generation resource；CI, jIt is branch i-j institute with idle
The quantity of compensation device.
Preferably, the equality constraint is shown below:
f(PLD, i, j, QLD, i, j, PG, i, j, CI, j)=0
In formula: PLD, i, jFor load reactive power；QLD, i, jFor load active power；PG, i, jFor distributed generation resource wattful power
Rate；CI, jFor compensation device quantity.
Preferably, the inequality constraints includes: voltage constraint, compensation device number constraint, distributed generation resource wattful power
Rate constraint, the constraint of distributed generation resource reactive power, the constraint of load active power and reactive load power constraint.
Preferably, the voltage constraint is shown below:
Umin< Ui< Umax
The compensation device number constraint is shown below:
Cmin< Ci< Cmax
The distributed generation resource active power constraint is shown below:
PG, min< PG, i, j< PG, max
The distributed generation resource reactive power constraint is shown below
QG, min< QG, i, j< QG, max
The load active power constraint is shown below:
PLD, min< PLD, i, j< PLD, max
The reactive load power constraint is shown below:
QLD, min< QLD, i, j< QLD, max
In formula: UminMost to descend voltage；UiFor voltage；UmaxFor maximum voltage；CminFor minimum compensation device quantity；CiTo mend
Repay device quantity；CmaxFor maximum compensation device quantity；PG, minFor minimum distributed generation resource active-power PG, i, jFor distributed electrical
Source active power；PG, maxTo be maximally distributed formula power supply active power；QG, minFor minimum distributed generation resource reactive power；QG, ijTo divide
Cloth power supply reactive power；QG, maxTo be maximally distributed formula power supply reactive power；PLD, minFor minimum load active power；PLD, i, jFor
Load active power；PLD, maxFor peak load active power；QLD, minFor minimum load reactive power；QLD, i, jFor reactive load
Power；QLD, maxFor peak load reactive power.
Preferably, described that the model is solved by particle swarm algorithm, it obtains optimal solution and realizes that power distribution network is idle
Optimization, comprising:
It will need to carry out reactive power in distribution network, the distributed generation resource of idle work optimization need to be carried out, it can in distribution network
A set is constituted with the reactive power compensator of configuration；
The set is brought into particle swarm algorithm model as the location sets of population；
By the way that inequality constraints punishment into particle swarm algorithm model, is constituted the fitness function of Co-PSO, and carry out
Iterative solution.
Preferably, the set is shown below:
In formula: xjIt is the position of j-th of state variable；Rand () is a randomizer；NsIt is state variable
Number；NlIt is the number for needing to carry out idle work optimization load in distribution network；NgIt is the distributed generation resource that need to carry out idle work optimization
Number；NcIt is the number for the capacitor that can be configured in distribution network.
Preferably, the fitness function of the Co-PSO is shown below:
gj(X) 0, j=1 <, 2,3 ... Nueq
In formula: f (X) is the objective function of idle work optimization；NueqFor the number of inequality constraints；gjIt (X) is inequality constraints
The set constituted；kfTo constrain out-of-limit penalty factor, biggish value is usually taken.
A kind of GA for reactive power optimization system towards multiple stochastic uncertainty, the system comprises:
Obtain module: for obtaining electric network data；
Construct module: for data to be brought into the idle work optimization model constructed in advance；
Computing module: for solving by particle swarm algorithm to the idle work optimization model, optimal solution realization is obtained
GA for reactive power optimization；
The idle work optimization model is with the minimum target of active loss, while equality constraint and inequality constraints.
Preferably, the building module, comprising: objective function and constraint condition submodule；
The objective function is shown below:
Min f=E (Ploss)=∑ E (PLoss, i, j(PI, j,QI, j,PG, i, j,QG, i, j, CI, j))
In formula: E () is expectation；PlossFor distribution network total losses；PLoss, i, jFor the loss of branch i-j in distribution network；
PI, jFor the active power of branch i-j institute on-load；QI, jFor the reactive power of branch i-j institute on-load；PG, i, jFor branch i-j institute
The active power of distributed generation resource；QG, i, jFor the reactive power of branch i-j institute distributed generation resource；CI, jIt is branch i-j institute with idle
The quantity of compensation device；
The constraint condition submodule, comprising: equality constraint and inequality constraints；
The equality constraint is shown below:
f(PLD, i, j, QLD, i, j, PG, i, j, CI, j)=0
In formula: PLD, i, jFor load reactive power；QLD, i, jFor load active power；PG, i, jFor distributed generation resource wattful power
Rate；CI, jFor compensation device quantity；
The inequality constraints include: voltage constraint, compensation device number constraint, distributed generation resource active power constraint,
The constraint of distributed generation resource reactive power, the constraint of load active power and reactive load power constraint；
The voltage constraint is shown below:
Umin< Ui< Umax
The compensation device number constraint is shown below:
Cmin< Ci< Cmax
The distributed generation resource active power constraint is shown below:
PG, min< PG, i, j< PG, max
The distributed generation resource reactive power constraint is shown below
QG, min< QG, i, j< QG, max
The load active power constraint is shown below:
PLD, min< PLD, i, j< PLD, max
The reactive load power constraint is shown below:
QLD, min< QLD, i, j< QLD, max
In formula: UminMost to descend voltage；UiFor voltage；UmaxFor maximum voltage；CminFor minimum compensation device quantity；CiTo mend
Repay device quantity；CmaxFor maximum compensation device quantity；PG, minFor minimum distributed generation resource active-power PG, i, jFor distributed electrical
Source active power；PG, maxTo be maximally distributed formula power supply active power；QG, minFor minimum distributed generation resource reactive power；QG, i, jFor
Distributed generation resource reactive power；QG, maxTo be maximally distributed formula power supply reactive power；PLD, minFor minimum load active power；PLD, i, j
For load active power；PLD, maxFor peak load active power；QLD, minFor minimum load reactive power；QLD, i, jFor load without
Function power；QLD, maxFor peak load reactive power.
Preferably, the computing module, comprising: particle swarm algorithm submodule and fitness function submodule；
The particle swarm algorithm submodule: for will need to carry out reactive power in distribution network, idle work optimization need to be carried out
Distributed generation resource, the reactive power compensator that can be configured in distribution network constitutes a set；
The set is brought into particle swarm algorithm model as the location sets of population；
By the way that inequality constraints punishment into particle swarm algorithm model, is constituted the fitness function of Co-PSO, and carry out
Iterative solution；
The fitness function of fitness function submodule, Co-PSO is shown below:
gj(X) 0, j=1 <, 2,3 ... Nueq
In formula: f (X) is the objective function of idle work optimization；NueqFor the number of inequality constraints；gjIt (X) is all inequality
Constrain constituted set；kfTo constrain out-of-limit penalty factor, biggish value is usually taken.
Compared with prior art, the invention has the benefit that
(1) a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty includes the following steps: to obtain power grid
Data；Data are brought into the idle work optimization model constructed in advance；The idle work optimization model is asked by particle swarm algorithm
Solution obtains optimal solution and realizes GA for reactive power optimization；The idle work optimization model is with the minimum target of active loss, while equation
Constraint and inequality constraints.The randomness of distribution network load, the randomness and idle benefit of distributed generation resource power output are considered simultaneously
The randomness of device is repaid, power distribution network actual conditions are more accurately reacted.
(2) cooperative particle swarm algorithm is introduced GA for reactive power optimization by technical solution provided by the invention, to every one-dimensional shape
State variable carries out local ropedancing, while carrying out global search to all local variables, and the two is combined, and efficiently solves
The problem of being easily trapped into local optimum during idle work optimization.
(3) technical solution provided by the invention finds that the present invention is in iteration by the comparison with traditional Reactive Power Optimization Algorithm for Tower
It has a clear superiority on number, optimal solution ratio.
Detailed description of the invention
Fig. 1 is a kind of var Optimization Method in Network Distribution flow chart towards multiple stochastic uncertainty of the invention；
Fig. 2 is particle swarm algorithm flow chart of the invention；
Specific embodiment
For a better understanding of the present invention, the contents of the present invention are done further with example with reference to the accompanying drawings of the specification
Explanation.
Embodiment 1:
The present invention devises a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty.The present invention examines simultaneously
Considering distribution network load, there is group number that puts into operation of randomness, the randomness that distributed power supply is contributed and reactive power compensator to have
The problems such as randomness, establish with the model of the minimum objective function of power distribution network active loss, using cooperative particle swarm algorithm into
Row solves, and realizes GA for reactive power optimization.The present invention has a clear superiority in the number of iterations, optimal solution ratio.
Specific example mode of the invention is specifically described with reference to the accompanying drawing.The present invention devises one kind towards more
The var Optimization Method in Network Distribution of weight stochastic uncertainty.The present invention considers that distribution network load has randomness, distribution simultaneously
Power supply power output randomness and group number that puts into operation of reactive power compensator there is the problems such as randomness, establishing has with power distribution network
The model of minimum objective function is lost in function, is solved using cooperative particle swarm algorithm, realizes asking for GA for reactive power optimization
Solution.The present invention has a clear superiority in the number of iterations, optimal solution ratio.
A kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty provided by the invention, including walk as follows
Suddenly, specific as shown in Figure 1；
Obtain electric network data；
Data are brought into the idle work optimization model constructed in advance；
The idle work optimization model is solved by particle swarm algorithm, optimal solution is obtained and realizes that power distribution network is idle excellent
Change；
The idle work optimization model is with the minimum target of active loss, while equality constraint and inequality constraints
(1) randomness of load
(1) randomness of load
Electric load is mainly made of induction conductivity, characterize induction conductivity operation characteristic parameter mainly effective percentage η,
Power factorWith revolving speed n, they are the functions of motor load rate β.Power factor declines with the reduction of load factor, and
And load factor is lower, power factor decline is faster.From the point of view of efficiency and power factor, the load factor β of induction conductivity is 0.75
It is optimal operating condition in~0.85 range.It is influenced by actual load variation, induction conductivity can be set and generally operated in
Load factor is in 0.7~0.9 range.When the load factor of induction conductivity changes within this range, from the function of induction conductivity
Rate factor property can be seen that power factorAmplitude of variation very little, can be approximately considered and float up and down ± 1%.
In this way, as the rated active power P of given induction conductivityNAnd power factorWhen, it may be assumed that it is practical to have
Function power P is in section [0.70PN, 0.9PN] on fluctuate, be denoted as [P]；And power factorInUpper variation, is denoted asThen the practical complex power 5 of induction conductivity is it is believed that in section [5]
Upper variation,
Wherein,
For lighting apparatus load, generally power factor can be roughly taken as 1.0.When the load of certain load point is by a variety of
When electrical equipment forms, statistical data can be calculated to the constant interval of mobility scale [P] and power factor of its total PayloadTo the constant interval [S] of the also complex power of the available load point.
(2) randomness of distributed generation resource power output
Renewable energy is many kinds of, and the randomness and intermittence of distributed generation resource are also different, in the present invention, choosing
Two kinds of typical distributed generation resources of wind-power electricity generation and photovoltaic power generation are selected, analyze its generated output characteristic, and establish its generated output
Interval model.
1) wind generator system
Wind is the motive power of wind power generating set, and the power output of blower is closely related with wind speed, studies wind-power electricity generation
Power producing characteristics, it is necessary first to study the distribution situation of wind speed.Wind speed profile model is distributed using Weibull (Weibull),.Wei Bu
Your distribution is a kind of asymmetrical distribution function cluster of unimodal two parameter, and probability density function can be described as:
Wherein, k is form factor, and value is embodied in Weibull distribution probability density function generally between 1.8~2.3
Curve shape on；μ is scale coefficient, indicates the mean wind speed of institute sector of observation in a certain amount of time.
It is generally believed that the power output of wind-driven generator is associated with the cube of wind speed at axial fan hub height:
Wherein, PRFor the nominal output of wind power generating set, PW(v) wind power generating set power when be wind speed being v goes out
Power, N are the quantity of wind-driven generator, and ρ is atmospheric density, CpFor energy conversion efficiency, R is the radius of the inswept area of wind wheel, VRFor
Rated wind speed, vciTo cut wind speed, vcoFor cut-out wind speed.When wind speed is greater than incision wind speed vciWhen, blower starting issues electric energy；
When wind speed is greater than rated wind speed VRWhen, blower keeps specified power output；When wind speed is less than incision wind speed vciOr higher than cutting out
Wind speed vcoWhen, blower is out of service and and grid disconnection.
The section modeling of wind-driven generator power output, that is, specify a section [Pw down, Pw up], so that it is met following two item
Part: 1) power output of wind-driven generator has sufficiently large probability to be in section [Pw down, Pw up] in；2) section [Pw down, Pw up] width
Degree is answered as small as possible, and at least it is the section of a limited width, is not a unlimited range.Probability Forms table can be passed through
Levy the requirement met needed for the interval model of wind-driven generator power output.
Wherein prob (A) indicates that event A is genuine probability, εwIt is that the power output of one and wind-driven generator is in interval model
In probability correlation parameter.For a determining εw, consider parameter Pw downAnd Pw upIt is difficult true by the probability distribution of wind speed
It cuts to obtain, the area of wind-driven generator power output can be obtained by the method for Monte Carlo simulation according to wind speed probability density function
Between model.Obviously, the characteristic of the probability density function based on wind speed chooses the corresponding wind-force of the biggish value of probability density function values
Generator output can make the width of gained interval model small as far as possible.
Wind-driven generator also issues reactive power while issuing active power, it is generally recognized that the power of wind-driven generator
Factor be it is determining, then by the active power interval model [P of wind-driven generatorw down, Pw up] its idle function can be calculated
Rate interval model [Qw down, Qw up]:
Wherein, cos θ w is the power factor of wind-driven generator.
2) photovoltaic generating system
The output power of photovoltaic generating system and suffered Intensity of the sunlight are closely related, therefore, Intensity of the sunlight
The randomness of variation brings randomness to the output power of photovoltaic generating system.The distribution of Intensity of the sunlight uses beta
(Beta) it is distributed.Beta is distributed to calculate intensity of illumination, and probability density function is as follows:
Wherein, s is Intensity of the sunlight, and value range is a≤s≤b, and a, b are respectively the sun in the time of somewhere section
The minimum value and maximum value of intensity of illumination,
Γ 0 is Gamma function.
Based on Intensity of the sunlight, the output power of photovoltaic generating system can be calculated according to above formula:
PPV=ξ × cos θ × ηm×Ap×ηp
Wherein, ξ is Intensity of the sunlight,For the incidence angle of sunlight；ηmFor MPPT efficiency；ApFor photovoltaic cell plate face
Product；ηpFor the transfer efficiency of photovoltaic cell.
It is similar to the section modeling method of wind-driven generator power output, a section [P can be usedPV down, PPV up] characterization light
The output power of photovoltaic generating system:
Wherein, εPVIt is the parameter of a probability correlation being in photovoltaic power generation system output power in interval model.Needle
To a determining εPV, consider parameter PPV downAnd PPV upIt is difficult definitely to obtain by the probability distribution of Intensity of the sunlight, it can be with
Photovoltaic power generation system output power is obtained by the method for Monte Carlo simulation according to Intensity of the sunlight probability density function
Interval model.Obviously, the probability density function characteristic based on Intensity of the sunlight chooses the biggish value of probability density function values
Corresponding photovoltaic power generation system output power can make the width of gained interval model small as far as possible.
It is different from wind-powered electricity generation, it is generally recognized that the power factor of grid-connected photovoltaic system be it is determining, pass through photovoltaic power generation
Active power interval model [Pw down, Pw up] its reactive power interval model [Q can be calculatedw down, Qw up]。
(3) randomness of reactive power compensator
Reactive power compensator in power distribution network can be divided into inductance and capacitor two types, and the present invention is by taking capacitor group as an example
It is analyzed.Capacitor group has the characteristics that capacity is big, element number is more, voltage class is high.Use shunt reactor group can be with
The reactive power compensation of route is carried out, and is to improve power transmission and transformation net stability limit and economy using series capacitor compensation technology
One of the effective means of property.
For the capacitor group of some load point, the capacity of each capacitor is certain.In other words, capacitor group is true
Fixed, the compensation capacity of capacitor is an equal difference array.
The randomness, the randomness of branch's power supply power output and the randomness of load for comprehensively considering reactive-load compensation equipment, build
The objective function of vertical idle work optimization
Objective function:
Min f=E (Ploss)=∑ E (PLoss, i, j(PI, j, QI, j, PG, i, j, QG, i, j, CI, j))
In formula,
Ploss-- distribution network total losses；
E () -- expectation；
PLoss, i, j-- the loss of branch i-j in distribution network；
PI, j-- the active power of branch i-j institute on-load；
QI, j-- the reactive power of branch i-j institute on-load；
PG, i, j-- the active power of branch i-j institute distributed generation resource；
QG, i, j-- the reactive power of branch i-j institute distributed generation resource；
CI, j-- branch i-j the capacity with reactive power compensator.
Constraint condition includes equality constraint and 6 two classes of inequality constraints, and equality constraint (trend constraint) is as follows:
f(PLD, ij, QLD, i, j, PG, ij, QG, ij, CI, j)=0
6 inequality constraints are as follows:
Umin< Ui< UmaxVoltage constraint
Cmin< Ci< CmaxCompensation device number constraint
PG, min< PG, i, j< PG, maxThe constraint of distributed generation resource active power
QG, min< QG, i, j< QG, maxThe constraint of distributed generation resource reactive power
PLD, min< PLD, i, j< PLD, maxThe constraint of load active power
QLD, min< QLD, i, j< QLD, maxReactive load power constraint
Using improving objective function that particle swarm algorithm establishes step 1 and constraint equation solves
(1) particle swarm optimization algorithm principle
Particle swarm optimization algorithm (Particle Swarm Optimization) as shown in Figure 2 is that simulation flock of birds was looked for food
A kind of novel bionic intelligent optimization algorithm migrated with proposed when collective behaviour in journey.In PSO, the solution of each optimization problem is just
It is a bird in search space, referred to as " particle ".All particles have the adaptive value determined by optimised function,
Each particle determines the direction and distance that they circle in the air there are one speed.In this way, particles can be current by following
Optimal particle scans in solution space, to obtain optimal solution.
In Particle Swarm Optimization Model, individual optimum point represents the desired positions crossed since emulation by this Individual Experience,
Neighborhood optimum point is the desired positions lived through by all neighbours of this individual, the two optimum points are by as attractor；It is a
Body has the memory of individual optimum point and neighborhood optimum point, it utilizes optimum point according to some simple rules according to a certain percentage
With the position that particle is adjusted at a distance from current location so that group gathers target proximity in certain the number of iterations.Its
Mathematical model are as follows:
In formula, vt+1、xt+1Speed and the position at t+1 moment are respectively indicated, w is inertial factor, c1For perception factor, c2For
The social factor, pbest are personal best particle, and gbest is global optimum position.
It can be seen that particle swarm algorithm by particles track pbest and gbest by above-mentioned two formula, constantly update to reach
Arrive search effect.And cooperative particle swarm is population using a kind of optimization method after co-architecture, cooperative particle swarm exists
The aspect of performance such as search quality, robustness have important performance boost.
(2) algorithm initialization
The original state variable of system population can be expressed as follows: wherein xjIt is the position of j-th of state variable, rand
() is a randomizer, NSIt is the number of state variable, NrIt is to need to carry out idle work optimization load in distribution network
Number, NgIt is the number that need to carry out the distributed generation resource of idle work optimization, NcIt is the number for the capacitor that can be configured in distribution network
Mesh.
(3) fitness function
Equality constraint is just met in population search process, by the way that inequality constraints is punished into objective function,
The fitness function that Co-PSO can be formed, is specifically expressed as follows:
gj(X) 0, j=1 <, 2,3 ..., Nueq
Wherein f (X) is the objective function of idle work optimization, NueqIt is the number of inequality constraints, gjIt (X) is all inequality
The set of constraint, kfIt is the out-of-limit penalty factor of constraint, usually takes biggish value.
(3) algorithm flow
The Co-PSO algorithm flow of proposition, which can summarize, to be as follows:
Step1: to each particle random initializtion position vector and velocity vector；
Step 2: the fitness value of entire population is recorded, saves as pbest and gbest respectively；
Step 3: population each time is iterated；
Step 4: each particle is iterated, process is as follows:
The fitness value of particle is assessed, and compared with pbest；
If
Otherwise, then
Terminate the processing to each particle；
Save new pbest；
·
Renewal speed vector sum position vector.
Step 5: if current the number of iterations reaches n times, main population shares gbest and gives seed subgroup, then receives
Collect new gbest vector；
Step 6: judging whether the condition of convergence meets,
Reach maximum number of iterations；
Difference of the fitness function within q generation is less than ε, i.e.,
|f(gbest(t))-f(gbest(t-h)) |≤ε, h=1,2 ..., q
If meeting the condition of convergence, stop iteration output result；Otherwise Step3 is gone to.
Embodiment 2:
Based on same design the present invention also provides a kind of GA for reactive power optimization system towards multiple stochastic uncertainty,
The system comprises:
Obtain module: for obtaining electric network data；
Construct module: for data to be brought into the idle work optimization model constructed in advance；
Computing module: for solving by particle swarm algorithm to the idle work optimization model, optimal solution realization is obtained
GA for reactive power optimization；
The idle work optimization model is with the minimum target of active loss, while equality constraint and inequality constraints.
The building module, comprising: objective function and constraint condition submodule；
The objective function is shown below:
Min f=E (Ploss)=∑ E (PLoss, i, j(PI, j, QI, j, PG, i, j, QG, i, j, CI, j))
In formula: E () is expectation；PlossFor distribution network total losses；PLoss, i, jFor the loss of branch i-j in distribution network；
PI, jFor the active power of branch i-j institute on-load；QI, j areThe reactive power of branch i-j institute on-load；PG, i, jFor branch i-j institute
The active power of distributed generation resource；QG, i, jFor the reactive power of branch i-j institute distributed generation resource；CI, jIt is branch i-j institute with idle
The quantity of compensation device；
The constraint condition submodule, comprising: equality constraint and inequality constraints；
The equality constraint is shown below:
f(PLD, i, j, QLD, i, j, PG, i, j, CI, j)=0
In formula: PLD, i, jFor load reactive power；QLD, i, jFor load active power；PG, i, jFor distributed generation resource wattful power
Rate；CI, jFor compensation device quantity；
The inequality constraints include: voltage constraint, compensation device number constraint, distributed generation resource active power constraint,
The constraint of distributed generation resource reactive power, the constraint of load active power and reactive load power constraint；
The voltage constraint is shown below:
Umin< Ui< Umax
The compensation device number constraint is shown below:
Cmin< Ci< Cmax
The distributed generation resource active power constraint is shown below:
PG, min< PG, i, j< PG, max
The distributed generation resource reactive power constraint is shown below
QG, min< QG, i, j< QG, max
The load active power constraint is shown below:
PLD, min< PLD, i, j< PLD, max
The reactive load power constraint is shown below:
QLD, min< QLD, i, j< QLD, max
In formula: UminMost to descend voltage；UiFor voltage；UmaxFor maximum voltage；CminFor minimum compensation device quantity；CiTo mend
Repay device quantity；CmaxFor maximum compensation device quantity；PG, minFor minimum distributed generation resource active-power PG, i, jFor distributed electrical
Source active power；PG, maxTo be maximally distributed formula power supply active power；QG, minFor minimum distributed generation resource reactive power；QG, i, jFor
Distributed generation resource reactive power；QG, maxTo be maximally distributed formula power supply reactive power；PLD, minFor minimum load active power；PLD, i, j
For load active power；PLD, maxFor peak load active power；QLD, minFor minimum load reactive power；QLD, i, jFor load without
Function power；QLD, maxFor peak load reactive power.
The computing module, comprising: particle swarm algorithm submodule and fitness function submodule；
The particle swarm algorithm submodule: for will need to carry out reactive power in distribution network, idle work optimization need to be carried out
Distributed generation resource, the reactive power compensator that can be configured in distribution network constitutes a set；
The set is brought into particle swarm algorithm model as the location sets of population；
By the way that inequality constraints punishment into particle swarm algorithm model, is constituted the fitness function of Co-PSO, and carry out
Iterative solution；
The fitness function of fitness function submodule, Co-PSO is shown below:
gj(X) 0, j=1 <, 2,3 ... Nueq
In formula: f (X) is the objective function of idle work optimization；NueqFor the number of inequality constraints；gjIt (X) is all inequality
Constrain constituted set；kfTo constrain out-of-limit penalty factor, biggish value is usually taken.
Obviously, described embodiments are some of the embodiments of the present invention, instead of all the embodiments.Based on the present invention
In embodiment, all other implementation obtained by those of ordinary skill in the art without making creative efforts
Example, shall fall within the protection scope of the present invention.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application
Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more,
The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces
The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application
Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions
The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs
Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real
The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates,
Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or
The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one
The step of function of being specified in a box or multiple boxes.
The above is only the embodiment of the present invention, are not intended to restrict the invention, all in the spirit and principles in the present invention
Within, any modification, equivalent substitution, improvement and etc. done, be all contained in apply pending scope of the presently claimed invention it
It is interior.
Claims (11)
1. a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty, which comprises the steps of:
Obtain electric network data；
Data are brought into the idle work optimization model constructed in advance；
The idle work optimization model is solved by particle swarm algorithm, optimal solution is obtained and realizes GA for reactive power optimization；
The idle work optimization model is with the minimum target of active loss, while equality constraint and inequality constraints.
2. a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty as described in claim 1, feature exist
In the objective function is shown below:
Min f=E (Ploss)=∑ E (PLoss, i, j(PI, j, QI, j, PG, i, j, QG, i, j, CI, j))
In formula: E () is expectation；PlossFor distribution network total losses；PLoss, i, jFor the loss of branch i-j in distribution network；PI, jFor
The active power of branch i-j institute on-load；QI, jFor the reactive power of branch i-j institute on-load；PG, i, jIt is distributed by branch i-j
The active power of formula power supply；QG, i, jFor the reactive power of branch i-j institute distributed generation resource；CI, jBy branch i-j band reactive compensation
The quantity of device.
3. a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty as described in claim 1, feature exist
In the equality constraint is shown below:
f(PLD, i, j, QLD, i, j, PG, i, j, CI, j)=0
In formula: PLD, i, jFor load reactive power；QLD, i, jFor load active power；PG, i, jFor distributed generation resource active power；CI, j
For compensation device quantity.
4. a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty as described in claim 1, feature exist
In the inequality constraints includes: voltage constraint, the constraint of compensation device number constraint, distributed generation resource active power, distribution
The constraint of power supply reactive power, the constraint of load active power and reactive load power constraint.
5. a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty as claimed in claim 4, feature exist
In the voltage constraint is shown below:
Umin< Ui< Umax
The compensation device number constraint is shown below:
Cmin< Ci< Cmax
The distributed generation resource active power constraint is shown below:
PG, min< PG, i, j< PG, max
The distributed generation resource reactive power constraint is shown below
QG, min< QG, i, j< QG, max
The load active power constraint is shown below:
PLD, min< PLD, i, j< PLD, max
The reactive load power constraint is shown below:
QLD, min< QLD, i, j< QLD, max
In formula: UminMost to descend voltage；UiFor voltage；UmaxFor maximum voltage；CminFor minimum compensation device quantity；CiFor compensation dress
Set quantity；CmaxFor maximum compensation device quantity；PG, minFor minimum distributed generation resource active-power PG, i, jHave for distributed generation resource
Function power；PG, maxTo be maximally distributed formula power supply active power；QG, minFor minimum distributed generation resource reactive power；QG, i, jFor distribution
Formula power supply reactive power；QG, maxTo be maximally distributed formula power supply reactive power；PLD, minFor minimum load active power；PLD, i, jIt is negative
Lotus active power；PLD, maxFor peak load active power；QLD, minFor minimum load reactive power；QLD, i, jFor reactive load function
Rate；QLD, maxFor peak load reactive power.
6. a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty as described in claim 1, feature exist
In, it is described that the model is solved by particle swarm algorithm, it obtains optimal solution and realizes GA for reactive power optimization, comprising:
It will need to carry out reactive power in distribution network, the distributed generation resource of idle work optimization need to be carried out, can be matched in distribution network
The reactive power compensator set constitutes a set；
The set is brought into particle swarm algorithm model as the location sets of population；
By the way that inequality constraints punishment into particle swarm algorithm model, is constituted the fitness function of Co-PSO, and be iterated
It solves.
7. a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty as claimed in claim 5, feature exist
In the set is shown below:
In formula: xjIt is the position of j-th of state variable；Rand () is a randomizer；NsIt is the number of state variable
Mesh；NlIt is the number for needing to carry out idle work optimization load in distribution network；NgIt is the number that need to carry out the distributed generation resource of idle work optimization
Mesh；NcIt is the number for the capacitor that can be configured in distribution network.
8. a kind of var Optimization Method in Network Distribution towards multiple stochastic uncertainty as claimed in claim 6, feature exist
In the fitness function of the Co-PSO is shown below:
gj(X) 0, j=1 <, 2,3 ... Nueq
In formula: f (X) is the objective function of idle work optimization；NueqFor the number of inequality constraints；gjIt (X) is inequality constraints institute structure
At set；kfTo constrain out-of-limit penalty factor, biggish value is usually taken.
9. a kind of GA for reactive power optimization system towards multiple stochastic uncertainty, which is characterized in that the system comprises:
Obtain module: for obtaining electric network data；
Construct module: for data to be brought into the idle work optimization model constructed in advance；
Computing module: it for being solved by particle swarm algorithm to the idle work optimization model, obtains optimal solution and realizes distribution
Net idle work optimization；
The idle work optimization model is with the minimum target of active loss, while equality constraint and inequality constraints.
10. a kind of GA for reactive power optimization system towards multiple stochastic uncertainty as claimed in claim 9, feature exist
In the building module, comprising: objective function and constraint condition submodule；
The objective function is shown below:
Minf=E (Ploss)=∑ E (PLoss, i, j(PI, j, QI, j, PG, i, j, QG, i, j, CI, j))
In formula: E () is expectation；PlossFor distribution network total losses；PLoss, i, jFor the loss of branch i-j in distribution network；PI, jFor
The active power of branch i-j institute on-load；QI, jFor the reactive power of branch i-j institute on-load；PG, i, jIt is distributed by branch i-j
The active power of formula power supply；QG, i, jFor the reactive power of branch i-j institute distributed generation resource；CI, jBy branch i-j band reactive compensation
The quantity of device；
The constraint condition submodule, comprising: equality constraint and inequality constraints；
The equality constraint is shown below:
f(PLD, i, j, QLD, i, j, PG, i, j, CI, j)=0
In formula: PLD, i, jFor load reactive power；QLD, i, jFor load active power；PG, i, jFor distributed generation resource active power；CI, j
For compensation device quantity；
The inequality constraints includes: voltage constraint, the constraint of compensation device number constraint, distributed generation resource active power, distribution
The constraint of formula power supply reactive power, the constraint of load active power and reactive load power constraint；
The voltage constraint is shown below:
Umin< Ui< Umax
The compensation device number constraint is shown below:
Cmin< Ci< Cmax
The distributed generation resource active power constraint is shown below:
PG, min< PG, i, j< PG, max
The distributed generation resource reactive power constraint is shown below
QG, min< QG, i, j< QG, max
The load active power constraint is shown below:
PLD, min< PLD, i, j< PLD, max
The reactive load power constraint is shown below:
QLD, min< QLD, i, j< QLD, max
In formula: UminMost to descend voltage；UiFor voltage；UmaxFor maximum voltage；CminFor minimum compensation device quantity；CiFor compensation dress
Set quantity；CmaxFor maximum compensation device quantity；PG, minFor minimum distributed generation resource active-power PG, i, jHave for distributed generation resource
Function power；PG, maxTo be maximally distributed formula power supply active power；QG, minFor minimum distributed generation resource reactive power；QG, i, jFor distribution
Formula power supply reactive power；QG, maxTo be maximally distributed formula power supply reactive power；PLD, minFor minimum load active power；PLD, i, jIt is negative
Lotus active power；PLD, maxFor peak load active power；QLD, minFor minimum load reactive power；QLD, i, jFor reactive load function
Rate；QLD, maxFor peak load reactive power.
11. a kind of GA for reactive power optimization system towards multiple stochastic uncertainty as claimed in claim 9, feature exist
In the computing module, comprising: particle swarm algorithm submodule and fitness function submodule；
The particle swarm algorithm submodule: for will need to carry out reactive power in distribution network, point of idle work optimization need to be carried out
Cloth power supply, the reactive power compensator that can be configured in distribution network constitute a set；
The set is brought into particle swarm algorithm model as the location sets of population；
By the way that inequality constraints punishment into particle swarm algorithm model, is constituted the fitness function of Co-PSO, and be iterated
It solves；
The fitness function of fitness function submodule, Co-PSO is shown below:
gj(X) 0, j=1 <, 2,3 ... Nueq
In formula: f (X) is the objective function of idle work optimization；NueqFor the number of inequality constraints；gjIt (X) is all inequality constraints
The set constituted；kfTo constrain out-of-limit penalty factor, biggish value is usually taken.
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| CN110880771A (en) * | 2019-12-11 | 2020-03-13 | 广东电网有限责任公司 | Transmission and distribution network reactive power optimization method and device |
| CN110930263A (en) * | 2019-11-15 | 2020-03-27 | 广东电网有限责任公司 | Medium-voltage distribution network short-circuit current calculation method containing photovoltaic power supply and induction motor based on black hole particle swarm algorithm |
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| CN110930263A (en) * | 2019-11-15 | 2020-03-27 | 广东电网有限责任公司 | Medium-voltage distribution network short-circuit current calculation method containing photovoltaic power supply and induction motor based on black hole particle swarm algorithm |
| CN110930263B (en) * | 2019-11-15 | 2020-12-29 | 广东电网有限责任公司 | Medium-voltage distribution network short-circuit current calculation method containing photovoltaic power supply and induction motor based on black hole particle swarm algorithm |
| CN110880771A (en) * | 2019-12-11 | 2020-03-13 | 广东电网有限责任公司 | Transmission and distribution network reactive power optimization method and device |
| CN110880771B (en) * | 2019-12-11 | 2022-01-11 | 广东电网有限责任公司 | Transmission and distribution network reactive power optimization method and device |
| CN114899825A (en) * | 2022-04-20 | 2022-08-12 | 国家电网有限公司技术学院分公司 | Power distribution network comprehensive optimization method based on network equivalent transformation |
| CN114899825B (en) * | 2022-04-20 | 2024-04-16 | 国家电网有限公司技术学院分公司 | Comprehensive optimization method for power distribution network based on network equivalent transformation |
| CN116154798A (en) * | 2023-03-30 | 2023-05-23 | 国网冀北电力有限公司 | Distribution network distributed static synchronous compensator configuration method based on digital twin |
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